The Blue Wave signals significant voter frustration with cost of living and inflation, giving Congress a mandate to prioritize affordability and cost-reduction measures. Economically, the rapid build-out of AI data centers is creating localized stress, driving up skilled labor wages and straining resources, while also facing constraints from power grids and potential local regulatory backlash. Policymakers must manage this 'sequencing issue'—addressing rising costs before full productivity gains from AI can materialize—and anticipate shifts in global data center infrastructure as the market matures.
The GUARD Financial Data Act Reflects a Misguided Pro-Regulatory Consensus
English Summary
The article argues that the proposed GUARD Financial Data Act is misguided because its over-regulatory approach undervalues data's utility, particularly in fighting fraud and developing AI services. Key provisions, such as data minimization and the right to deletion, could severely impede anti-fraud measures and innovative credit risk assessments that rely on comprehensive data sets. Furthermore, the Act fails to address the core issue of law enforcement's warrantless access to financial data, and its screen scraping rules create security vulnerabilities. Policymakers should therefore abandon broad federal over-regulation and instead focus on targeting specific, tangible harms caused by bad actors, while reforming law enforcement's data access powers.
中文摘要
本文論點指出，擬議的《GUARD金融資料法案》存在誤導性，因為其過度監管的思維會低估資料的實用價值，尤其是在打擊詐騙和發展人工智慧服務方面。法案中的關鍵規定，例如資料最小化和刪除權，可能會嚴重阻礙依賴全面資料集進行的反詐騙措施和創新信用風險評估。此外，該法案未能解決執法部門無需令狀存取金融資料的核心問題，且其螢幕抓取（screen scraping）規定也製造了安全漏洞。因此，政策制定者應放棄廣泛的聯邦過度監管，轉而專注於針對不良行為者造成的特定、具體危害，同時改革執法部門的資料存取權限。
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